Methods and systems for analyzing the quality of digital signature confirmation images

ABSTRACT

Methods and systems for evaluating an imager that produces bi-chrome images from a scanner or a digital imaging device, the bi-chrome images having pixels of a first and second color. In one embodiment, a method includes generating an image with a hand-held imaging device, the image having pixels of a first color and a second color, analyzing the image to determine information about particles of the first and second color contained in the image, each particle comprising contiguous pixels of the same color, the particle information comprising information on first and second color particle size and count, and determining if the image is unacceptable based on predetermined objective criteria and the particle information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/445,245, filed on Apr. 12, 2012, which is a continuation of U.S.application Ser. No. 12/118,460, filed on May 9, 2008, both of which arehereby incorporated by reference in their entirety.

BACKGROUND

1. Field

The field of the invention relates to analyzing the quality of an imageand, more specifically, to evaluating the quality of a digital bi-chromesignature confirmation image.

2. Background

Delivery services, including commercial package delivery services, theUnited States Postal Service, and couriers, often receive a recipient'ssignature as confirmation that a package, letter or another deliverableitem was successfully delivered. The confirmation signature is typicallysaved by the delivery service. Subsequently, the confirmation signaturecan be used to verify the package was delivered and identify whoaccepted the package. In some delivery services, a confirmationsignature is entered into a delivery tracking system using a stylus anda signature pad. In other delivery services, the recipient signs a pieceof paper to confirm the delivery, and the recipients' handwrittenconfirmation signature is then digitally imaged or scanned andelectronically stored in a delivery tracking system.

The quality of a confirmation signature image can be affected by manyfactors. Certain image aberrations can make the stored image inadequatefor identifying the recipient. For example, movement of the imagingdevice, sunlight, shadows, and/or a tilted imaging angle can adverselyaffect the resulting confirmation signature image. Image processingtechniques can be used to enhance the quality of a confirmationsignature image after it is stored. However, a real-time evaluation ofthe confirmation signature image to determine if the image is acceptablemay obviate the need for subsequent processing. Also, it can bedifficult to evaluate how well any one certain technique will work for alarge set of confirmation signature images (for example, 1000+ images),which again indicates the need for evaluating a confirmation signatureimage before it is stored. Also, determining how a software upgradeaffects the quality of an imaged signature for a large set of images canbe difficult. Typically, the images are evaluated by a user viewing theimages. Such an evaluation is quite burdensome and subjective.

Accordingly, implementing a real-time confirmation signature evaluationprocess that rejects poor images when a signature is first imaged wouldbe advantageous. In addition, methods for validating a new image process(e.g., a software or hardware upgrade) that produces an image of aconfirmation signature would be useful to address the above-describedproblems and other problems in the art.

SUMMARY OF CERTAIN EMBODIMENTS

The system, method, and devices of the invention each have severalaspects, no single one of which is solely responsible for its desirableattributes. Without limiting the scope of this disclosure, its moreprominent features will now be discussed briefly. After considering thisdiscussion, and particularly after reading the section entitled“Detailed Description of Certain Embodiments” one will better understandthe inventive features and aspects of these embodiments.

The features of the embodiments described herein can be usedadvantageously in many ways. Some embodiments can be used for rejectingpoor quality images at the time the signatures are scanned to ensuresufficiently high quality data is input into a tracking system. Also,some embodiments validate an imaging process at a point in time after aconfirmation signature image has been input into a tracking system. Inaddition, some embodiments can validate an imaging process differenthardware and software releases used to generate a confirmation signatureimage, thereby determining if the new release is acceptable as areplacement for the currently used hardware or software.

In some embodiments, a method of evaluating a bi-chrome digital imagegenerated by an imaging device includes generating an image with ahand-held imaging device, the image having pixels of a first color and asecond color, determining information about particles of the first andsecond color contained in the image, each particle comprising contiguouspixels of the same color, the particle information comprisinginformation on first and second color particle size and count, anddetermining if the image is unacceptable based on predeterminedobjective criteria and the particle information. The first color can beblack and the second color can be white. In some aspects, the particleinformation includes the area of the image covered by small first colorparticles, the number of small first color particles, the number ofsmall second color particles in the image, the area of the image coveredby first color particles, the area of a selected region in the imagecovered by non-small first color particles, the area of the imagecovered by large first color particles, the number of non-small firstcolor particles in the selected region, the number of first colorparticles having a first defined range of sizes, the average size of thefirst color particles having a first defined range of sizes, and thearea of the image covered by the first color particles having a firstdefined range of sizes. For confirmation signatures, the selected regionmay be a signature portion.

According to another aspect of the first embodiment, the small firstcolor particles comprise particles of the first color having a number ofpixels that is less than about a first threshold value, the large firstcolor particles comprise particles of the first color having a number ofpixels greater than about a second threshold value, the small secondcolor particles comprise particles of the second color having a numberof pixels less than about a third threshold value, and the non-smallfirst color particles comprise particles having a number of pixelswithin a defined range, the range comprising between about a fourththreshold value and about a fifth threshold value. In another aspect,the image is unacceptable if any of the objective criteria is met, theobjective criteria comprising (a) the (total number of pixels) of theimage covered by small first color particles is greater than about asixth threshold value and the number of small first color particles isgreater than about a seventh threshold value, (b) the number of smallsecond color particles is greater than about an eighth threshold value,(c) the percentage of the area of the image covered by non-small firstcolor particles is less than about a ninth threshold value, (d) thepercentage of the area of the image covered by non-small first colorparticles is greater than about a tenth threshold value, (e) thepercentage of the area of a selected region in the image covered bynon-small first color particles is less than about an eleventh thresholdvalue, (f) the total area (total number of pixels) of the image coveredby of large first color particles in the image is greater than about atwelfth threshold value and the number of non-small first colorparticles in the selected area is greater than about a thirteenththreshold value, (g) the number of first color particles having adefined range of sizes is greater than about a fourteenth thresholdvalue and first color particles having a defined range of sizes has anaverage pixel size about a fifteenth threshold value, (h) the total areaof the image covered by first color particles having a defined range ofsizes is greater than about a sixteenth threshold value, and (i) thepercentage of the area in a selected region in the image covered bynon-small first color particles is greater than about a seventeenththreshold value.

In some embodiments, and in reference to the above-stated thresholdvalues, the first threshold value is about 20; the second thresholdvalue is about 30,000; the third threshold value is about 20; the fourththreshold value is about 21; the fifth threshold value is about 200; thesixth threshold value is about 2500; the seventh threshold value isabout 500; the eighth threshold value is about 175; the ninth thresholdvalue is about 7%; the tenth threshold value is about 35%; the elevenththreshold value is about 5.3%; the twelfth threshold value is about45000; the thirteenth threshold value is about 15; the fourteenththreshold value is about 55; the fifteenth threshold value is about 65;the sixteenth threshold value is about 5000; and the seventeenththreshold is about 35%.

Another embodiment includes a machine readable medium comprisinginstructions for evaluating a bi-chrome image produced by an imager, thebi-chrome images having pixels of a first and second color, wherein theinstructions upon execution cause a machine to generate an image with animaging device, the image having pixels of a first color and a secondcolor, determine information about particles of the first and secondcolor contained in the image, each particle comprising contiguous pixelsof the same color, the particle information comprising information onfirst and second color particle size and count, and determine if theimage is unacceptable based on predetermined objective criteria and theparticle information. In one aspect, the particle information includesthe area of the image (or total number of pixels) covered by small firstcolor particles, the number of small first color particles, number ofsmall second color particles in the image, the area of the image coveredby non-small first color particles, the area of a selected region in theimage covered by non-small first color particles, the area of the imagecovered by large first color particles, the number of non-small firstcolor particles in the selected region, the number of first colorparticles having a first defined range of sizes, the average size of thefirst color particles having a first defined range of sizes, and thearea of the image covered by the first color particles having a firstdefined range of sizes.

Another embodiment includes a system for generating and evaluatingbi-chrome digital images includes an imaging device configured tocapture a digital bi-chrome image, the image having particles of a firstcolor and particles of a second color, each particle comprisingcontiguous pixels of the same color, a processor configured to analyzeat least a portion of the bi-chrome image and determine informationabout the particles of the first and second color, the particleinformation comprising information on first and second color particlesize and count, and further configured to determine if the image isunacceptable in real-time or near real-time based on predeterminedobjective criteria and the determined particle information. Typically,the first color is black and the second color is white. In someembodiments, the first color is white and the second color is black.Other colors may also be considered the first color and second color aswell. The particle information for the analyzed portion of the image mayinclude the area covered by small first color particles, the number ofsmall first color particles, number of small second color particles inthe image, the area covered by non-small first color particles, the areaof a selected region in the analyzed portion of the image covered bynon-small first color particles, the area of the analyzed image coveredby large first color particles, the number of non-small first colorparticles in the selected region, the number of first color particleshaving a first defined range of sizes, the average size of the firstcolor particles having a first defined range of sizes, and the area ofthe analyzed portion of the image covered by the first color particleshaving a first defined range of sizes.

Another embodiment includes a method of evaluating an imagingconfiguration that produces digital bi-chrome images using a controlprocess, the test imaging configuration having test hardware and/or testsoftware elements, comprises processing a plurality of bi-chrome testimages with a test imaging configuration to form a plurality ofresulting bi-chrome test images, analyzing the plurality of resultingtest images to determine information about pixel particles of a firstcolor and second color contained in each of the resulting test images,each particle having contiguous pixels of the same color, the determinedparticle information comprising, for each resulting test image, sizeinformation of first color particles and second color particles in theimage and quantity information of the number of first color particlesand second color particles in each image determining the number ofresulting test images that are unacceptable based on predeterminedobjective criteria and the particle information, processing theplurality of test images with a control imaging configuration to form aplurality of resulting control images, analyzing the plurality ofresulting control images to determine information about pixel particlesof a first color and second color contained in each of the resultingcontrol images, each particle having contiguous pixels of the samecolor, the determined particle information comprising, for eachresulting control image, size information of first color particles andsecond color particles in the image and quantity information of thenumber of first color particles and second color particles in eachimage, determining the number of resulting control images that areunacceptable based on predetermined objective criteria and the particleinformation, and comparing the number of unacceptable resulting controlimages and unacceptable resulting test images to determine if the testimaging configuration is acceptable.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustrating a confirmation signature imagingsystem.

FIG. 2 is a flowchart illustrating a process for determining the qualityof an image.

FIG. 3 is a flowchart illustrating a process for determining if animaging configuration is acceptable.

FIG. 4 is an illustration of an unacceptable image due to noise in theimage which is manifested as small black particles, each particle formedby a number of contiguous black pixels.

FIG. 5 is a portion of the image in FIG. 4 illustrating an example ofblack pixel noise.

FIG. 6 is an image that illustrates an acceptable amount of black pixelnoise.

FIG. 7 is a flowchart illustrating a process for determining images withunacceptable or acceptable amounts of black pixel noise.

FIG. 8 is an illustration of an unacceptable image due to white pixelnoise.

FIG. 9 is a portion of the image in FIG. 8 illustrating an example ofwhite pixel noise.

FIG. 10 is an image that illustrates an acceptable amount of white pixelnoise.

FIG. 11 is a flowchart illustrating a process for determining imageswith unacceptable or acceptable amounts of white pixel noise.

FIG. 12 is an illustration of an unacceptable “blank” image.

FIG. 13 is an illustration of an acceptable “non-blank” image, accordingto some embodiments.

FIG. 14 is a flowchart illustrating a process for determiningunacceptable “blank” images.

FIG. 15 is an image that illustrates an example of incorrect subjectmatter in a confirmation signature image which results in a large countof black pixels.

FIG. 16 is an illustration of an image that has an acceptable blackpixel count.

FIG. 17 is a flowchart illustrating a process for determining whether animage has a high number of black pixels.

FIG. 18 is an illustration of an unacceptable image produced by anIntelligent Mail Device (“IMD”) imager.

FIG. 19 is an illustration of a control image produced using a flatbedscanner.

FIG. 20 is a flowchart illustrating a process for determining whetherthe quality of a test image is similar to the quality of a controlimage.

FIG. 21 illustrates the signature and printed name area of interest on atest image and a control image.

FIG. 22 is an illustration of an unacceptable image due to at least onelarge black particle.

FIG. 23 is an illustration of an acceptable image based on certainthreshold limitations.

FIG. 24 is a flowchart illustrating a process for determining whether animage is unacceptable due to the presence of at least one large blackparticle.

FIG. 25 is an illustration of an unacceptable image due to the presenceof small to medium sized black pixel particles above a certainthreshold.

FIG. 26 is an illustration of an acceptable image that does not containsmall to medium sized black pixel particles above a certain threshold.

FIG. 27 is a flowchart illustrating a process for determining whether animage is unacceptable due to certain thresholds for small tomedium-sized black particles.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

The following detailed description is directed to certain specificembodiments of the development. In this description, reference is madeto the drawings wherein like parts or steps may be designated with likenumerals throughout for clarity. Reference in this specification to “oneembodiment,” “an embodiment,” or “in some embodiments” means that aparticular feature, structure, or characteristic described in connectionwith the embodiment is included in at least one embodiment of theinvention. The appearances of the phrases “one embodiment,” “anembodiment,” or “in some embodiments” in various places in thespecification are not necessarily all referring to the same embodiment,nor are separate or alternative embodiments mutually exclusive of otherembodiments. Moreover, various features are described which may beexhibited by some embodiments and not by others. Similarly, variousrequirements are described which may be requirements for someembodiments but not other embodiments.

Millions of confirmation signatures are digitally recorded. However, toproperly document that a package was successfully delivered and identifythe recipient, the resulting image must be legible. Some embodiments ofthe development can be used to determine whether a confirmationsignature image is “acceptable” or “unacceptable” when the image isgenerated. This allows another confirmation signature image to begenerated immediately if the first image is unacceptable, and obviatesverification problems arising from storing an image that can not be usedto identify the signed recipient.

Some embodiments, described hereinbelow, determine information relatingto groups of contiguous pixels or “particles” in a confirmationsignature image, and can evaluate the information against certainobjective criteria to determine the acceptability of the image. Suchembodiments can similarly be used to evaluate bi-chrome images otherthan confirmation signature images, and also similarly in color imageswhere a particle can be defined to comprise pixels of a single color orrange of colors.

The quality of a digital image is due at least in part to the hardwareand software comprising the image system that is used to generate thedigital image. For example, the quality of a digital image can beaffected by the imaging sensor and the software that processes the datagenerated by the sensor. Handheld imaging systems may be tasked toproduce acceptable images under a variety of lighting conditions.Accordingly, data relating to the number of acceptable and/orunacceptable images produced by an imaging system under variedconditions can provide information that relates to the performance ofthe imaging system. In this determination, each image can be analyzedfor particular characteristics, and then predetermined objectivecriteria can be used to evaluate the characteristics and determine ifthe quality of each image is acceptable.

Some embodiments can be used for analyzing the quality of United StatesPostal Service hand-written digital signature confirmation images.Images of confirmation signatures can be produced by a handheld scannersuch as, for example, an Intelligent Mail Device (“IMD”) scanner used byU.S. Postal workers in the field. Typically, the size of a signatureconfirmation image generated by the IMD is 282,112 pixels (928pixels×304 pixels). Different size image and images other than signatureconfirmation images can also be evaluated by the embodiments describedherein. Some examples herein describe area coverage of an image,referring to the 282,112 pixel image produced by the IMD. Of course,equivalent area coverage can be determined for other size images, andthe images can be evaluated using this determined area coverage. Suchequivalent area coverage may be expressed in pixels, or as an equivalentpercentage of the image.

Some embodiments can be used to test new imaging configurations. Forexample, an imaging system with a first configuration of imaginghardware and/or software can be used to produce a first set of images.Then, an imaging system having a different configuration of hardwareand/or software can be used to produce a second set of images. Forexample, the “different” configuration may be a hardware or software“upgrade” that requires validation before it is installed system-wide.Identical objective criteria can be used to evaluate the first andsecond set of images to determine which images are “acceptable” for eachconfiguration. Image particle analysis techniques are used to determineinformation about particles in an image, e.g., particle counts, averagesize particles of a certain color, and total area coverage or fractionalarea coverage (e.g., percentage) of a certain color pixel particle.Other criteria of the image such as “skew” can also be determined.Evaluation of this information can then be used to determine an image'sacceptability. Comparing the number of “acceptable” (or “unacceptable”)images in the first and second sets of images can help determine if anew hardware or software configuration is an improvement over theexisting configuration.

Some imaging systems produce bi-chrome or two-color images. For example,the IMD produces bi-chrome images. In such cases, objective criteriarelating to the number of black or white “particles” in the image isused to evaluate the images. “Particle” as used herein is a broad termthat refers to a group of two or more contiguous pixels of the samecolor (for example, black or white). Particles may be generally referredto herein as being small, medium, or large in size, generally referringto the number of contiguous pixels (of the same color) in the particle.A particle can also be referred to as “non-small” which indicates theparticle may be larger than a small particle. Similarly, a particle canbe referred to as “non-large” which indicates the particle is smallerthan a large particle. In some embodiments, a “small” particle cancomprise less than about twenty (20) pixels. In some embodiments, a“large particle” can comprise greater than about 10,000, 20,000, orabout 30,000 pixels. Embodiments described herein include examples ofthe particle size used in certain objective criteria for imageevaluation. For example, particle size may be used as a threshold valuewhen determining the number of particles of a particular size (e.g.,small, medium, or large), sometimes referred to as the particle “count.”Particle size may also be used to identify a certain set of particles,and the area of the image that is covered by the particles in the setmay be used to evaluate whether the image is acceptable.

FIG. 1 is a schematic diagram showing an example of an imaging system 1that can benefit from using image quality evaluation methods describedherein. This imaging system 1 illustrates an imaging system forcapturing confirmation signature images. Such imaging systems can beused by commercial package delivery companies, the United States PostalService, intra-company delivery services, and other organizations thattypically require a signature confirmation 3 in a designated areaportion of a delivery receipt 2, for example, on a delivered package oron a document associated with a package or other item. The signatureconfirmation in FIG. 1 includes a recipient's signature and printedname. The portion of the image that depicts the confirmation signatureis referred to herein as an “area of interest” to differentiate it fromthe rest of the image. Package, as used herein, is a broad term thatgenerally refers to anything that can be delivered, including but notlimited to, a letter, package, parcel, product, or good). Thedevelopment can also be used for any signature image of a confirmationreceipt, for example, for a payment or a service.

An imaging device 4 is used to capture an image of the recipient'ssignature. For delivery services the imaging device 4 may be handheld.The imaging device 4 includes an optical component 6 which isillustrated, in dashed lines, as disposed on the side of the imagingdevice 4 that faces into the page. The optical component 6 receiveslight and captures a digital image of all or part of the deliveryreceipt including the confirmation signature. Although the image can becolor, typically it is black-and-white (or bi-chrome) to save on storagespace and facilitate faster transmission. In some embodiments theoptical component 6 includes a CCD (charge-coupled device) image sensoror a CMOS (complimentary metal-oxide semiconductor) image sensor. Theimaging device 4 can also include a user interface 5 that allows a userto operate the imaging device 4. The imaging device 4 can be builtspecifically for capturing confirmation signatures and can include otherfunctionality as well. The user interface can be used to inputinformation relating to a delivery (“delivery information”) to theimaging device and can be associated with the imaged confirmationsignature. One example of a suitable imaging device is the IntelligentMail Device scanner currently used by the United States Postal Service.In other embodiments, suitable imaging functionality can be included inmulti-purpose devices including portable telecommunication devices(mobile phones, cell phones, digital cameras) and other portabledevices.

FIG. 1 also illustrates a tracking system 7 that is configured toreceive delivery data, including the delivery information andconfirmation signature images from the imaging device 4. The deliverydata is sent to the tracking system 7 through a (wired or wireless)communication network. The delivery data can be sent immediately, or innear real-time, or it can be downloaded and provided to the trackingsystem 7 at a later time, for example, at the end of a delivery run. Thetracking system 7 stores the delivery data in a database 8. The database8 may be a structured query language (SQL) relational database residingwithin the tracking system 7. The database 8 may implement T-SQL,PL/SQL, Sqsh, SQL, SQL/PSM, SQL PL, MySQL, PL/pgSQL, or otheralternative structured query language standards. As one of skill in theart will appreciate, other types of databases and other schemes of datastorage may also be used.

In some embodiments, the tracking system 7 illustrated in FIG. 1 canreceive and track delivery data from numerous imaging devices. Thequality of confirmation signature images captured by the imaging deviceor stored in the tracking system 7/database 8 can be analyzed usingpredetermined objective criteria, discussed hereinbelow, to determinewhether the images are acceptable. Information on the number ofacceptable images, and specific image characteristics of the acceptableand unacceptable images, can be used to evaluate the quality of theimaging hardware and software that is used to generate the confirmationsignature images. This information can be especially valuable whentesting software (or hardware) upgrades to see if in fact the “upgrade”performs better than its predecessor. The objective criteria can also beused to ensure acceptable images are captured by the imaging device 4before the images are stored to a tracking system. In some embodiments,the images are evaluated on the imaging device 4 and the operator isalerted as to the quality of the image, thus allowing another image tobe generated, if necessary.

FIG. 2 is a flowchart illustrating a process 20 for determining thequality of an image or sets of images. Embodiments of processes thatinclude particular objective criteria for evaluating image quality arediscussed herein below in reference to FIGS. 3-27. At step 21 of process20, images are generated by imaging or scanning data, for example,delivery confirmation signatures. Imaging device 4 (FIG. 1) or anotherimaging device can be used to generate the image. At step 22, the imageis analyzed, and information needed to evaluate objective criteria isdetermined for each image, and the objective criteria is calculated foreach image.

For example, objective criteria relating to determining if there is toomuch black pixel noise in the image may use information of the number ofsmall black pixel particles in the image, and also the total areacovered by the small black pixel particles (e.g., percentage of theimage or area of interest). At step 23, the criteria is evaluated todetermine if the image is acceptable. For example, this can includeevaluating the number of small black pixel particles and the total areaof small black pixel particles relative to threshold values to determineif the image is acceptable. If the image satisfies the objectivecriteria, it is deemed acceptable at step 24. If not, the image isrejected at step 25. Process 20 can then be repeated to evaluate eachimage or each image in a set of images. Acceptable images may then bestored or further processed. When process 20 is used on a portableimaging device, the process can provide real-time feedback to theoperator indicating whether an image is accepted or rejected, allowinganother image to be made for rejected images.

In another example, the amount of skew of the generated image is used todetermine whether of not the image is acceptable. Skew, as used herein,refers to the angle between a vertical or horizontal axis of thescanning device and a corresponding vertical or horizontal axis in theimage. Typical acceptable skew values may be up to about ten degrees. Ina preferred embodiment, a skew value above which an image is deemedunacceptable is about plus or minus seven degrees. In some embodiments,a feature in the image that has a known rectangular shape is evaluatedto see if it appears rectangular in the image. If it is not a rectanglein the image, the imaging system may be able to manipulate the image sothat the feature appears as a rectangle. If the feature cannot beadequately restored to a rectangular shape, the image may be deemed“unacceptable” and rejected. In some examples, the feature is a barcode.When used, skew is typically combined with one or more other criteria todetermine if an image is acceptable. In some embodiments, the imagingsystem also compensates for image aberrations of pitch, roll, and yaw.

FIG. 3 is a flowchart illustrating a process 30 for determining if animaging configuration is acceptable. One example of when this processcan be used is when a new version of imaging software is delivered tothe user entity. Before the new software is placed on all of theentities' imaging devices, a test can be done to determine if“upgrading” to the new software provides a performance improvement thatis worth the resources it takes to accomplish the upgrade, and to checkto see if the new software even works under a variety of imagingconditions. At step 31, a plurality of images are scanned or obtained(from previously scanned and stored images). At step 32, objectivecriteria is applied to each of the images to determine if each image inthe set of images is acceptable. At step 33, the ratio of acceptableimages verses the total number of images is evaluated to determine ifthe ratio is sufficient, for example, if it is above a predeterminedthreshold value. The threshold value can be based on a previousevaluation of the same images using a different software configuration.For example, the prior (or current) software configuration can be usedto determine which of the images are acceptable or rejected. In someembodiments, the ratio of acceptable images relates to the ratio ofacceptable to unacceptable images (or vice-versa). If the thresholdvalue or ratio is sufficient, at step 34 the new scan configuration isaccepted. If not, the scan configuration is rejected at step 35.

Image Particle Evaluation

It has been found that evaluating images based on certain particleanalysis criteria allows differentiation between “acceptable” and“unacceptable” images that are used for signature confirmation. Examplesof the objective criteria described herein relate directly to whetherparticles of a particular size are found, or not found, in imagesgenerated by an IMD. However, they also relate more generally to anybi-chrome image generated using other imaging devices. Bi-chrome or twocolor images can be black and white, or any other two colors. Referenceshereon to a pixel or particle of a “first color” and/or a “second color”refer to one of the two colors of a bi-chrome image. For example, thefirst color can be black and the second color can be white. Similartechniques can also be used for color images.

The size of the particle refers to the area covered by its contiguouspixels of a particular color, for example, in square pixels. The size ofthe particles can be equivalently described by referring to the numberof pixels in a particle or the area covered by the pixels. The criteriacan relate to black particles and/or white particles. The terms small,medium, and large are broad terms that generally refer to particleshaving pixel counts of less than about 20 pixels (small), between about20 and about 200-600 pixels (medium), and larger than about10,000-30,000 pixels (large). It has been found that pixels in thesesize ranges are typically the most relevant for two-color imageevaluation. In some embodiments, medium-sized particles can include allparticle sizes between small particles and large particles. It isappreciated that differences in evaluation criteria of one or severalpixels may not affect the evaluation, and that the actual criteria bestused may differ based on the types of images analyzed. Analyzingbi-chrome images using analysis methods that include one or more of theparticle evaluation criteria is advantageous over typical imageevaluation techniques. The methods facilitate quick, objectiveevaluation of numerous images, and allow different image generationconfigurations to be compared in a statistically significant manner. Thesame (or similar) criteria described for IMD images can be used foranalyzing images in other imaging applications and on images created byother digital imaging or scanning devices.

Examples of processes and criteria for evaluating the quality ofbi-chrome images are described under the headings “Black Pixel Noise,”“White Pixel Noise,” Too Few Black Pixels,” “High Number of BlackPixels,” “Test Image/Control Image Comparison,” “Large Black ParticleAberrations,” and “Medium Black Particles” herein below. Some examplesof criteria can include, but are not limited to, the amount of blackpixel noise in the entire IMD image (for example, particles less thanabout 20 pixels), the amount of white pixel noise in the entire IMDimage (for example, particles less than about 20 pixels), and/or thenumber of black pixel particles in the entire IMD image (for example,particles greater than about 20 pixels). “Noise” in an image generallyrefers to unwanted high frequency aberrations of one color within anarea of a second color. For example, black pixels speckled throughout awhite background. Other criteria can include the amount of black pixelparticles in a certain IMD image area of interest (for example,particles greater than about 20 pixels), the amount of black pixelparticles in the IMD image area of interest as compared to acorresponding image area in an image generated by a flatbed scannedimage (for example, particles greater than about 20 pixels), the amountof black pixel particles in the entire IMD image (for example, whereparticles greater than about 30,000 pixels), and/or the number orcoverage of medium black pixel particles in the entire IMD image (forexample, where the particles are medium-sized, for example between about20-200 pixels).

Such criteria can be used in combination or singly or as separatecriterion in different embodiments. Specific values, for example, about30,000 pixels, can be used as threshold values in the applying criteria.It is appreciated that other threshold values from the specific valuesdisclosed herein may also be used for effective image evaluation. Insome cases, the threshold values may be determined in part by theparticular technical application in which they are used. Certaincriteria is described individually in detail below, and may be used withany of the processes described herein to assess image quality. Typicallythe threshold values are predetermined. However, in some embodiments thethreshold values can be dynamically determined, based either on a userinput or a predetermined image analysis strategy and real-time data.

Evaluating and analyzing the confirmation signature images need not beperformed only on the imaging device or computers of the tracking system7. In some embodiments the images are moved from the tracking system 7to another computer system suitably configured to execute the imagequality evaluation software. For example a server system, or astandalone computer including a desktop or notebook computer, can beused to execute the image quality evaluation software.

Black Pixel Noise

In one example, an image is evaluated to determine the amount of blackpixel noise in the image. FIG. 4 is an illustration of an unacceptableimage 40 due to excessive black pixel noise manifested in small blackparticles. FIG. 5 illustrates an example of a close-up view of blackpixel noise 50 showing the numerous small black pixel particles. FIG. 6illustrates an image 60 that exhibits a small amount of black pixelnoise that may result in the image 60 being deemed “acceptable” based onselected threshold values. In one embodiment, an image is analyzed todetermine the number of black particles in the image that are less thana certain size (as determined by the number of contiguous pixels in theparticle). In some examples, the particle size is between about 10 andabout 30 pixels, preferably between about 15 and about 25 pixels, and insome examples is about 20 pixels.

FIG. 7 is a flowchart illustrating a process 70 for determining whetheran image has an unacceptable or acceptable amount of black pixel noisein an image generated by an IMD. Process 70, and the processesillustrated in FIGS. 11, 14, 17, 20, 24 and 27, can be used to determineif the images are acceptable in step 23 of the flowchart shown in FIG.2. At step 71, process 70 determines the number of small black pixelparticles in the image. In one example, each small black pixel particlesis defined to be a set of contiguous pixels such that each particle iscomprised of less than about 20 black pixels. In some embodiments, thethreshold value for the number of particles is between about 250 andabout 750 particles, more preferably between about 450 and about 550particles. In some embodiments, the particle count threshold value isabout 500. At step 72, process 70 determines the total area of all thesmall black pixel particles. The total area is the cumulative areacovered by black pixels in all of the small black pixel particles.Accordingly, typically the total area equals the total number of blackpixels in all the small black pixel particles. Threshold values toevaluate the area covered by small black pixel particles and the pixelcount are predetermined or dynamically calculated. These values can beinfluenced by the image size because larger images will have a largernumber of small black pixel particles. In some embodiments, thethreshold value for the total area of black pixels in all the particlesis between about 2,000 and 3,000 pixels, and more preferably betweenabout 2,400 and 2,600 pixels. In some embodiments, the total areathreshold value is set to about 2,500 pixels. At step 73, theacceptability of the image is determined by comparing the thresholdvalues for the black pixel particle count and the total black pixelparticle area to the actual values determined for this criteria. In oneexample, the image is deemed “unacceptable” if the total black pixelcount (or area of noise) in the image exceeds the threshold value ofabout 2,500 pixels and the small black particle count exceeds thethreshold value of about 500.

White Pixel Noise

An image can also be evaluated for white pixel noise by determining thenumber of small white particles in the image. Each white particlecomprises contiguous white pixels. A large number of small white pixelparticles can indicate aberrations in the image background, which istypically depicted in the image as white. FIG. 8 shows an example of anunacceptable image 80 due to white pixel noise. FIG. 9 illustrates whitepixel noise in an image 90 where the image is shown in a magnified view.In contrast to the image shown in FIG. 8, FIG. 10 illustrates an image100 that has an acceptable level of white pixel noise. In someembodiments, the threshold value for the number of white pixel particlesis between about 100 and about 250, more preferably between about 150and about 200, and typically at about 175.

FIG. 11 is a flowchart illustrating a process 110 for determiningwhether an image is unacceptable or acceptable based on the amount ofwhite pixel noise in the image. At step 111, process 110 analyzes theimage to determine the number of (small) white particles (the particlecount), where the number of contiguous white pixels in each particle areless than a certain threshold value, for example, about 20 pixels. Atstep 112, process 110 compares particle count to a predeterminedthreshold value. In some embodiments the threshold value is about 175.If the particle count is greater than the corresponding threshold value,process 110 rejects the image; if less than the threshold value process110 accepts the image.

Too Few Black Pixels

To be useful, the confirmation signature must be legible. Accordingly, aconfirmation signature image that is blank (or nearly so) cannot be usedto verify who the signor is, and is therefore unacceptable. FIG. 12illustrates an unacceptable image 120, which is deemed a “blank” imagedue to the nearly nonexistent signature. To be acceptable, the signatureshould be visible against the background, as shown in the acceptableimage 130 in FIG. 13. FIG. 14 illustrates a flowchart of a process 140that can be used to determine whether a signature confirmation image isan unacceptable “blank” image. To identify “blank” images, the imagesare analyzed to determine the area covered by non-small and non-mediumsize black pixel particles in the entire image and in the area ofinterest (for example, the signature and printed name area of theimage). At step 141, the process 140 determines the total area coverageof non-small and non-medium size black pixel particles in the entireimage. At step 142, process 140 determines the area covered by non-smalland non-medium size black pixel particles in the area of interest. Atstep 143, predetermined threshold values are compared to the determinedvalues in steps 141 and 142.

In one example, if the black pixel area coverage in the entire image asdetermined by step 141 is less than about 7%, or the black pixel areacoverage in the area of interest is less than about 5.3%, the image isdeemed unacceptable. In other embodiments, the threshold value for theblack pixel coverage by non-small and non-medium black pixel particlesin the entire image area is set to between about 6% and about 10%, andset for the area of interest to between about 5% and about 9%. Thethreshold value for the area of interest is preferably set below thethreshold value for the entire image.

High Number of Black Pixels

Having a large number of black pixels in an image can indicate that theimage is unacceptable for signature confirmation. This condition mayoccur if there is too much noise in an image or if the images are darkor dimly lit. Images that do not correctly depict the area of interest(which typically has a light background), instead showing a differentportion of the delivery receipt or package which may have a darkerbackground, may also have a large number of black pixels. For example,an image 150, illustrated in FIG. 15, incorrectly depicts a barcodeinstead of a confirmation signature area. The barcode image 150 includesa large number of black pixels, where an acceptable image 160 (FIG. 16)of the confirmation signature image may not.

FIG. 17 illustrates a process 170 for determining if an image isunacceptable due to having a large number of black pixels. Thisdetermination can be made for the entire image, or for a selectedportion of the image. At step 171, process 170 analyzes the entire imageto determine the number of pixels in all the black particles in theimage regardless of the size of the particles, (e.g., all of the blackpixels in the image) and then determines the percentage of the area ofthe image covered by the black pixels. In some embodiments, the coverageof black pixels can be made for an area of interest in the image. Atstep 172, process 170 compares a predetermined threshold value (forexample, a percentage value) to the calculated percentage of black pixelcoverage. If black pixels cover a higher percentage of the image (or theselected area of interest) than the threshold value, the image is deemedunacceptable. In some embodiments, the threshold value is between about20% and about 40%, and in some preferable embodiments about 35%.

Test Image/Control Image Comparison

In some embodiments, images that are generated using two differentsystems can be compared to determine performance information of the twosystems. For example, a test image can be generated using a software orhardware upgrade to determine if it is actually an improvement to theexisting (or previous) version. For proper evaluation, numerous imagescan be compared. Automating the image comparison allows hundreds or eventhousands of images to be evaluated, saving numerous man-hours, makingthe results more statistically significant, and increasing theobjectivity of the evaluation.

In one embodiment, one or more images generated from an IMD (forexample, image 180 of FIG. 18) are compared to one or more imagesgenerated using a flat-bed scanner (for example, image 190 of FIG. 19).In this case, the IMD image 180 is considered the test image and theflat-bed image 190 is considered the control image (and is expected tobe a higher quality).

FIG. 20 illustrates a process 200 which compares a test image and acontrol image. At step 201 the process 200 determines the area coveredby black pixels in medium to large-sized black pixel particles that aregreater in size than a threshold value, in the signature and printedname area of the test image and the control image. In some embodiments,the threshold value is about 20 pixels. In some embodiments, theassociated test image and the control image depict the same signatureand printed name to obviate differences caused by different signaturesor printed names.

FIG. 21 illustrates an example of an area of interest that can beevaluated and used to compare the two images. Referring again to FIG.20, at step 202, the process 200 compares the black pixel area coveredin the test image and the control image. The control image can beassumed to be better due to the conditions in which the image wasformed. In this example, the test image is considered unacceptable ifthe black pixel area coverage difference is less than a threshold value,for example 15%, as this indicates there are too many differencesbetween the test image and the control image.

A similar process can be also used to test a new hardware or softwareconfiguration of the imaging device. Here, the control images are imagesmade from the current (older) configuration and the test images areimages made with the new configuration. One or more of the criteriadescribed herein can be used, singly or in combination, to determine ifthe test images have a higher objective quality over the control imagesby exhibiting less black noise, white noise, blank images, black pixelarea coverage, large black particles and/or small-to-medium sized blackparticles.

Large Black Particle Aberrations

The presence of a large black particle in the image, such as the blackdefect shown in the image 220 in FIG. 22, can also indicate that animage is unacceptable for signature confirmation. However, some imagesthat have a black particle defect may be acceptable because a sufficientportion of the area of interest is legible, as illustrated in image 230of FIG. 23.

FIG. 24 is a flowchart illustrating a process 240 that can determinewhether an image is unacceptable due to the presence of at least onelarge black particle, or if it is truncated due to an imaging orprocessing error. At step 241, process 240 determines the number oflarge black particles in the image and also determines the area coveredby the large pixels. The threshold value for defining the minimum sizeof a large particle can be about 200 pixels or larger, including betweenabout 10,000 pixels to about 50,000 pixels or larger. In someembodiments, the size of a large particle is at least about 20,000pixels, and preferably at least about 30,000 pixels. At step 242 theprocess 240 determines the number of non-small particles in the area ofinterest of the image. Typically, the area of interest is the signatureconfirmation area of the image. Non-small particles include medium-sizedparticles and large particles. At step 243, the process 240 compares thetotal large black particle pixel coverage area to a threshold (forexample, about 45,000). The process 240 also compares the large blackparticle count in the area of interest to another threshold (forexample, about 15). In this example, if the non-small black pixelcoverage area of interest exceeds 45,000 pixels and the particle countin the signature area exceeds 15 the image is deemed unacceptable.

Medium Black Particles

The presence of medium-size black pixel particles can also indicate thatan signature confirmation image is unacceptable. FIG. 25 illustratesthree examples of signature portions of signature confirmation images,each being unacceptable due to the presence of medium-sized blackparticles. FIG. 26 illustrates acceptable signature confirmation imagesthat include some medium-sized black pixel particles but not enough toreject the image.

FIG. 27 is a flowchart illustrating a process 270 for determiningwhether an image is unacceptable due to the presence of medium-sizedblack particles as determined by certain objective criteria andpredetermined thresholds. The criteria used for process 270 targetsimages with poor clarity, significantly blurred images, and imageshaving significant bleed-though to be identified as unacceptable images.At step 271, process 270 determines the number of medium-sized blackpixel particles in the entire image. At step 272, process 270 determinesthe total area covered by all the medium-sized black particles. At step273, process 270 determines the average pixel count of the medium blackpixel particles. Then at step 274, the process compares the number ofmedium-sized black particles, the black pixel particle coverage area andaverage pixel area per particle to predetermine threshold values.

The development is not meant to be limited by the exemplary embodimentsdescribed herein for delivered packages of any kind. Rather, similarembodiments can be used for remote authorization for any good or servicethat requires a signature. For example, rental return services such ascar rentals, equipment rentals, or rentals at the beach. Embodiments canbe used for door-to-door sales or services, including for goods thatwill be delivered subsequently or services that may be performed at alater time. Other applications of the development include instanceswhere a signature is used for identify verification including at remotelocations or events.

The methods described here can be implemented using one or morecomputers configured to execute one or more computer program embodyingthe desired method. The computer programs can be provided as computerprogram products comprising a computer useable medium having computerprogram logic recorded thereon, which when executed by a computerprocessor configured to execute the same, performs an authenticationmethod according to the invention. The computer program logic cancomprise computer program code logic configured to perform a series ofoperations required to implement the particular embodiment desired.Computer usable medium refers to any medium or device that can be usedto provide software or program instructions to a computer or computersystem, and includes media such as removable data storage devices. Thecomputer usable medium also includes a machine readable mediumcomprising instructions for performing an image evaluation methodaccording to the invention that upon execution causes a machine toexecute the image evaluation method. In one embodiment, the program canbe implemented on a hand-held imaging device, for example, an IMD. Asthose in the art will appreciate, the embodiments, features, andfunctionality of the development as described are not dependent onparticular computer system or processor architecture or on a particularoperating system. The development can also be implemented using othercomputer or processor systems and/or architectures.

Computer programs or computer control logic can be stored in a memory incommunication with the processor(s) intended to execute the program orcan be received via any suitable communications interface. Computerprograms executed according to the invention can enable the computersystem to perform the desired functions. In embodiments where themethods of the development are implemented using software, the softwarecan be stored in, or transmitted via, a computer program product andloaded into a computer system using any suitable approach, including aremovable storage device, hard drive, or communications interface. Whenthe control logic or software is executed by the processor(s), theprocessor(s) are caused to perform the functions of the invention. Inother embodiments, the invention is implemented primarily in hardwareusing, for example, hardware components such as PALs, applicationspecific integrated circuits (ASICs), or other hardware components.

The development illustratively described herein suitably may bepracticed in the absence of any element(s) not specifically disclosedherein. The terms and expressions which have been employed are used asterms of description and not of limitation, and there is no intentionthat in the use of such terms and expressions of excluding anyequivalents of the features shown and described or portions thereof, butit is recognized that various modifications are possible within thescope of the invention claimed. Thus, it should be understood thatalthough the development has been specifically disclosed by certainembodiments and optional features, modification and variation of theconcepts herein disclosed may be resorted to by those skilled in theart, and that such modifications and variations are considered to bewithin the scope of this invention as defined by the appended claims.

1. A method of evaluating two bi-chrome digital images generated by animaging device, the method comprising: generating a first image, theimage having pixels of a first color and a second color; identifying aparticle comprising contiguous pixels of the first color in the firstimage; determining information on the particle size and the particlelocation of particles of the first color contained in the first image;generating a second image, the image having pixels of a first color anda second color; identifying a particle comprising contiguous pixels ofthe first color in the second image; determining information on theparticle size and the particle location of particles of the first colorcontained in the second image; developing a first value corresponding tothe area of the first image covered by particles of a first color;developing a second value corresponding to the area of the second imagecovered by particles of a first color; and determining if the secondimage is acceptable based on a predetermined objective criteria and thefirst and second values.